AI-Induced Empathy
Can we use AI to increase empathy for victims of far-away disasters by making our homes appear similar to the homes of victims?
Deep Empathy gets you closer to the realities of those that suffer the most, by helping you imagine what neighbourhoods around the world would look like if hit by a disaster.
Around the world, 50 million children have migrated across borders or been forcibly displaced within their own countries. In Syria alone, the brutal six-year old war has affected more than 13.5 million people and 80% of the country's children—8.4 million young lives shattered by violence and fear. Hundreds of thousands of people have been displaced and their homes destroyed.
Can you conceptualize these numbers? People generate a response that statistics can't. And technologists—through tools like AI—have opportunities to help people see things differently.
Deep Empathy utilizes deep learning to learn the characteristics of Syrian neighborhoods affected by conflict, and then simulates how cities around the world would look in the midst of a similar conflict. Can this approach -- familiar in a range of artistic applications -- help us to see recognizable elements of our lives through the lens of those experiencing vastly different circumstances, theoretically a world away? And by helping an AI learn empathy, can this AI teach us to care?
Explore How Your City Would Look Like
London, England
Chicago, USA
Melbourne, Australia
Paris, France
Boston, USA
Tokyo, Japan
Toronto, Canada
San Francisco, USA
Amsterdam, Netherlands
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Explore the globe
Can empathy be facilitated through images that combine the familiarity of the cities we live in with traces of the realities of those that live far away?
Can AI help make that translation at scale? Can you imagine similar disasters happening worldwide?
Click on the globe to explore
Help our AI to learn empathy
Which image makes you feel more empathic towards victims of Syria crisis?Help our AI to learn empathy: select which images are more likely to inspire empathy and help us train our algorithm to get better.
Click here to Take the Survey
(Survey images might contain sensitive and graphic content.)
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Timeline of Empathy and AI
Can we increase empathy for victims of
far-away disasters using AI to create images that simulate disasters closer to home?
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Turing Test
Alan Turing develops a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.
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Voight Kampff Test
Science fiction writer Philip K Dick imagines the ‘voight-kampff’ test in his novel ‘Do Androids Dream of Electric Sheep?’ The test questions an AI’s ability to feel empathy.
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Empathy
“Considerable research shows that we are more likely to help someone in need when we ‘feel for’ that person.” C. Daniel Batson, social psychologist.
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Affective Computing
Rosalind Picard, computer scientist from the MIT Media Lab coins the term ‘Affective Computing’. It refers to computing that relates to, arises from or influences emotions.
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Identifiable Victim Effect
Decision Scientist Karen Jenni and Economist George Lowenstein provide evidence that humans' ability to scale empathy is constrained by our greater willingness to respond to individual victims of whom we know specific details.
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Psychic Numbing
Psychologist Paul Slovic shows that people become "numbly indifferent to the plight of individuals" of large-scale disasters and atrocities. Statistics surrounding such events "fail to spark emotion or feeling and thus fail to motivate action."
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Deep Learning
Deep learning becomes feasible, which leads to machine learning becoming integral to many widely used software services and applications.
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Alan Kurdi
A picture of Syrian refugee Alan Kurdi’s lifeless body on the beach published. Research finds "...an iconic photo of a single child had more impact than statistical reports of hundreds of thousands of deaths."
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Deep Empathy
Can we increase empathy for victims of far-away disasters using Google’s machine-learning algorithm to create images that simulate disasters closer to home?
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Help us
teach AI empathy!
Pinar Yanardag
Post-doc at Scalable Cooperation, MIT Media Lab
Iyad Rahwan
Associate Professor at Scalable Cooperation, MIT Media Lab
Manuel Garcia Herranz
Chief Scientist at Ventures, UNICEF Office of Innovation
Christopher Fabian
Lead of Ventures, UNICEF Office of Innovation
Zoe Rahwan
Research Associate at London School of Economics
Nick Obradovich
Research Scientist at Scalable Cooperation, MIT Media Lab
Abhimanyu Dubey
Graduate Student at Scalable Cooperation, MIT Media Lab
Manuel Cebrian
Research Scientist at Scalable Cooperation, MIT Media Lab
Special thanks to: Barbara Fasolo from London School of Economics, and Alissa Collins from UNICEF.